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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Experiments on visual loop closing using vocabulary trees
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Ankita Kumar, GRASP Laboratory, University of Pennsylvania, 3330 Walnut Street, L402, Philadelphia, 19104, USA
Jean-Philippe Tardif, GRASP Laboratory, University of Pennsylvania, 3330 Walnut Street, L402, Philadelphia, 19104, USA
Roy Anati, GRASP Laboratory, University of Pennsylvania, 3330 Walnut Street, L402, Philadelphia, 19104, USA
Kostas Daniilidis, GRASP Laboratory, University of Pennsylvania, 3330 Walnut Street, L402, Philadelphia, 19104, USA
In this paper we study the problem of visual loop closing for long trajectories in an urban environment. We use GPS positioning only to narrow down the search area and use pre-built vocabulary trees to find the best matching image in this search area. Geometric consistency is then used to prune out the bad matches. We compare several vocabulary trees on a sequence of 6.5 kilometers. We experiment with Hierarchical K-means based trees as well as Extremely Randomized Trees and compare results obtained using five different trees. We obtain the best results using Extremely Randomized Trees. After enforcing geometric consistency the matched images look promising for Structure from Motion applications.
Citation:
Ankita Kumar, Jean-Philippe Tardif, Roy Anati, Kostas Daniilidis, "Experiments on visual loop closing using vocabulary trees," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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